Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: identifying a target customer population of a series product and dividing into segments by customer behaviors relevant to adoption of and compliance to a series of purchases of the series product. A marketing campaign strategy for each segment is devised and executed, and adoption rate and compliance rate is predicted by analytical modeling and later evaluated by actual sales data. Parameters used in predicting the adoption rate and the compliance rate are adjusted for accuracy.

Abstract: The methods include, for instance: building model connections between models in a knowledgebase, which stores case data as model networks. An exploration probability stored in the knowledgebase indicates a likelihood of new connections based on a case data input to be employed for an analytical model of the case data input, which includes numerous stages and multiple model choices in each stage. Based on the new connections and model networks of the knowledgebase, paths are created and performance of respective paths/connections are evaluated. A predefined number of top performing paths are selected and the models and attributes that do not appear in the top performing paths are eliminated from the knowledgebase. Probabilities of model networks and a future case data input are updated accordingly and a best-fit model for the case data input is presented to a user.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: evaluating a brand value as a function of numerous parameters as formulated by brand value dynamics, and by use of input data for accurate prediction of the brand value. A brand equity is estimated based on the brand value and brand values of all brands competing in the market.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: building model connections between models in a knowledgebase, which stores case data as model networks. An exploration probability stored in the knowledgebase indicates a likelihood of new connections based on a case data input to be employed for an analytical model of the case data input, which includes numerous stages and multiple model choices in each stage. Based on the new connections and model networks of the knowledgebase, paths are created and performance of respective paths/connections are evaluated. A predefined number of top performing paths are selected and the models and attributes that do not appear in the top performing paths are eliminated from the knowledgebase. Probabilities of model networks and a future case data input are updated accordingly and a best-fit model for the case data input is presented to a user.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: predicting a market share based on consumer preference shift based on inputs of including sales data of respective branded products in a market, product feature data, and product event data. Feature cluster switch rates are first estimated and then brand switch rate within a subject feature cluster is estimated. Future market share of a branded product having the subject feature cluster is predicted and reported.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: identifying a target customer population of a series product and dividing into segments by customer behaviors relevant to adoption of and compliance to a series of purchases of the series product. A marketing campaign strategy for each segment is devised and executed, and adoption rate and compliance rate is predicted by analytical modeling and later evaluated by actual sales data. Parameters used in predicting the adoption rate and the compliance rate are adjusted for accuracy.

Abstract: A network of nodes is constructed from data obtained from a data source of a social medium. A node corresponds to a medical professional. From the data, a likelihood is determined of the node prescribing a product. From the data, for a period, a level of knowledge is computed of the node about the product. A change in the level of knowledge of the node from a previous period is determined. Using a change in a level of knowledge corresponding to each node in the network, an amount of knowledge reinforcement to be applied to each node in the network is computed. A knowledge reinforcement resource to perform knowledge reinforcement at a subset of the nodes is allocated according to a schedule, where the allocated knowledge reinforcement resource to the node has a correspondence with the change in the level of knowledge of the node.